In the generative AI era, Large Language Models struggle with complex data ecosystems, causing inaccurate queries, hallucinations, and slow debugging that hinder productivity and delay insights. This breakout session shows how integrating Databricks with dbt via the Model Context Protocol solves these issues. By exposing dbt’s metadata—models, tests, and lineage—LLMs gain structured context for precise, schema-aware responses. We’ll walk through setting up a dbt MCP server on Databricks Unity Catalog, enabling dynamic querying of dbt artifacts and reducing manual intervention. In real-world use, this DBX + dbt integration cut LLM query errors, sped up data exploration, and improved efficiency for faster, AI-driven analytics—boosting forecasting and cost optimization without vendor lock-in. Looking ahead, MCP enables a future where humans and LLMs co-pilot data workflows, using dbt’s evolving semantic layer to fuel autonomous agents and intuitive, real-time analytics conversations.